Aurélie C. Lozano

ICML Conference 2025 Conference Paper

Aligning Protein Conformation Ensemble Generation with Physical Feedback

• Jiarui Lu
• Xiaoyin Chen
• Stephen Zhewen Lu
• Aurélie C. Lozano
• Vijil Chenthamarakshan
• Payel Das
• Jian Tang 0005

Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.

ICML Conference 2024 Conference Paper

Larimar: Large Language Models with Episodic Memory Control

• Payel Das
• Subhajit Chaudhury
• Elliot Nelson
• Igor Melnyk
• Sarathkrishna Swaminathan
• Sihui Dai
• Aurélie C. Lozano
• Georgios Kollias

Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar’s memory allows for dynamic, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed—yielding speed-ups of 8-10x depending on the base LLM —as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting, information leakage prevention, and input context length generalization with Larimar and show their effectiveness. Our code is available at https: //github. com/IBM/larimar.

ICLR Conference 2023 Conference Paper

Protein Representation Learning by Geometric Structure Pretraining

• Zuobai Zhang
• Minghao Xu
• Arian Rokkum Jamasb
• Vijil Chenthamarakshan
• Aurélie C. Lozano
• Payel Das
• Jian Tang 0005

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://github.com/DeepGraphLearning/GearNet.

ICML Conference 2019 Conference Paper

Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning

• Jihun Yun
• Peng Zheng 0002
• Eunho Yang
• Aurélie C. Lozano
• Aleksandr Y. Aravkin

We study high-dimensional estimators with the trimmed $\ell_1$ penalty, which leaves the h largest parameter entries penalty-free. While optimization techniques for this nonconvex penalty have been studied, the statistical properties have not yet been analyzed. We present the first statistical analyses for M-estimation, and characterize support recovery, $\ell_\infty$ and $\ell_2$ error of the trimmed $\ell_1$ estimates as a function of the trimming parameter h. Our results show different regimes based on how h compares to the true support size. Our second contribution is a new algorithm for the trimmed regularization problem, which has the same theoretical convergence rate as difference of convex (DC) algorithms, but in practice is faster and finds lower objective values. Empirical evaluation of $\ell_1$ trimming for sparse linear regression and graphical model estimation indicate that trimmed $\ell_1$ can outperform vanilla $\ell_1$ and non-convex alternatives. Our last contribution is to show that the trimmed penalty is beneficial beyond M-estimation, and yields promising results for two deep learning tasks: input structures recovery and network sparsification.

ICML Conference 2017 Conference Paper

Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity

• Eunho Yang
• Aurélie C. Lozano

Imposing sparse + group-sparse superposition structures in high-dimensional parameter estimation is known to provide flexible regularization that is more realistic for many real-world problems. For example, such a superposition enables partially-shared support sets in multi-task learning, thereby striking the right balance between parameter overlap across tasks and task specificity. Existing theoretical results on estimation consistency, however, are problematic as they require too stringent an assumption: the incoherence between sparse and group-sparse superposed components. In this paper, we fill the gap between the practical success and suboptimal analysis of sparse + group-sparse models, by providing the first consistency results that do not require unrealistic assumptions. We also study non-convex counterparts of sparse + group-sparse models. Interestingly, we show that these are guaranteed to recover the true support set under much milder conditions and with smaller sample size than convex models, which might be critical in practical applications as illustrated by our experiments.

ICML Conference 2014 Conference Paper

Elementary Estimators for High-Dimensional Linear Regression

• Eunho Yang
• Aurélie C. Lozano
• Pradeep Ravikumar

We consider the problem of structurally constrained high-dimensional linear regression. This has attracted considerable attention over the last decade, with state of the art statistical estimators based on solving regularized convex programs. While these typically non-smooth convex programs can be solved in polynomial time, scaling the state of the art optimization methods to very large-scale problems is an ongoing and rich area of research. In this paper, we attempt to address this scaling issue at the source, by asking whether one can build \emphsimpler possibly closed-form estimators, that yet come with statistical guarantees that are nonetheless comparable to regularized likelihood estimators! We answer this question in the affirmative, with variants of the classical ridge and OLS (ordinary least squares estimators) for linear regression. We analyze our estimators in the high-dimensional setting, and moreover provide empirical corroboration of its performance on simulated as well as real world microarray data.

ICML Conference 2014 Conference Paper

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments

• Eunho Yang
• Aurélie C. Lozano
• Pradeep Ravikumar

We consider the problem of estimating distributional parameters that are expected values of given feature functions. We are interested in recovery under high-dimensional regimes, where the number of variables p is potentially larger than the number of samples n, and where we need to impose structural constraints upon the parameters. In a natural distributional setting for this problem, the feature functions comprise the sufficient statistics of an exponential family, so that the problem would entail estimating structured moments of exponential family distributions. A special case of the above involves estimating the covariance matrix of a random vector, and where the natural distributional setting would correspond to the multivariate Gaussian distribution. Unlike the inverse covariance estimation case, we show that the regularized MLEs for covariance estimation, as well as natural Dantzig variants, are \emphnon-convex, even when the regularization functions themselves are convex; with the same holding for the general structured moment case. We propose a class of elementary convex estimators, that in many cases are available in \emphclosed-form, for estimating general structured moments. We then provide a unified statistical analysis of our class of estimators. Finally, we demonstrate the applicability of our class of estimators on real-world climatology and biology datasets.

UAI Conference 2013 Conference Paper

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality

• Vikas Sindhwani
• Ha Quang Minh
• Aurélie C. Lozano

We propose a general matrix-valued multiple kernel learning framework for highdimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. We develop a highly scalable and eigendecompositionfree algorithm that orchestrates two inexact solvers for simultaneously learning both the input and output components of separable matrix-valued kernels. As a key application enabled by our framework, we show how high-dimensional causal inference tasks can be naturally cast as sparse function estimation problems, leading to novel nonlinear extensions of a class of Graphical Granger Causality techniques. Our algorithmic developments and extensive empirical studies are complemented by theoretical analyses in terms of Rademacher generalization bounds.

ICML Conference 2012 Conference Paper

Multi-level Lasso for Sparse Multi-task Regression

• Aurélie C. Lozano
• Grzegorz Swirszcz

ICML Conference 2010 Conference Paper

Learning Temporal Causal Graphs for Relational Time-Series Analysis

• Yan Liu 0002
• Alexandru Niculescu-Mizil
• Aurélie C. Lozano
• Yong Lu